Integer Programming for Bayesian Network Structure Learning

James Cussens

Integer Programming for Bayesian Network Structure Learning

James Cussens

Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

Bayesian networks provide an attractive representation of structured probabilistic information. There is thus much interest in ‘learning’ BNs from data. In this paper the problem of learning a Bayesian network using integer programming is presented. The SCIP (Solving Constraint Integer Programming) framework is used to do this. Although cutting planes are a key ingredient in our approach, primal heuristics and efficient propagation are also important.

Original language	English
Pages (from-to)	99-110
Number of pages	12
Journal	Quality Technology and Quantitative Management
Volume	11
Issue number	1
Publication status	Published - Mar 2014

Keywords

Bayesian networks
Integer programming
Machine learning

Access to Document

https://doi.org/10.1080/16843703.2014.11673328

Cite this

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